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Monte Carlo scenario generation for retail loan portfolios

Author

Listed:
  • J L Breeden

    (Strategic Analytics Inc.)

  • D Ingram

    (Strategic Analytics Inc.)

Abstract

Monte Carlo simulation is a common method for studying the volatility of market traded instruments. It is less employed in retail lending, because of the inherent nonlinearities in consumer behaviour. In this paper, we use the approach of Dual-time Dynamics to separate loan performance dynamics into three components: a maturation function of months-on-books, an exogenous function of calendar date, and a quality function of vintage origination date. The exogenous function captures the impacts from the macroeconomic environment. Therefore, we want to generate scenarios for the possible futures of these environmental impacts. To generate such scenarios, we must go beyond the random walk methods most commonly applied in the analysis of market-traded instruments. Retail portfolios exhibit autocorrelation structure and variance growth with time that requires more complex modelling. This paper is aimed at practical application and describes work using ARMA and ARIMA models for scenario generation, rules for selecting the correct model form given the input data, and validation methods on the scenario generation. We find when the goal is capturing the future volatility via Monte Carlo scenario generation, that model selection does not follow the same rules as for forecasting. Consequently, tests more appropriate to reproducing volatility are proposed, which assure that distributions of scenarios have the proper statistical characteristics. These results are supported by studies of the variance growth properties of macroeconomic variables and theoretical calculations of the variance growth properties of various models. We also provide studies on historical data showing the impact of training length on model accuracy and the existence of differences between macroeconomic epochs.

Suggested Citation

  • J L Breeden & D Ingram, 2010. "Monte Carlo scenario generation for retail loan portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 399-410, March.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:3:d:10.1057_jors.2009.105
    DOI: 10.1057/jors.2009.105
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    References listed on IDEAS

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    Cited by:

    1. Afful-Dadzie, Anthony & Mallett, Alexandra & Afful-Dadzie, Eric, 2020. "The challenge of energy transition in the Global South: The case of electricity generation planning in Ghana," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    2. Afful-Dadzie, Anthony & Afful-Dadzie, Eric & Awudu, Iddrisu & Banuro, Joseph Kwaku, 2017. "Power generation capacity planning under budget constraint in developing countries," Applied Energy, Elsevier, vol. 188(C), pages 71-82.
    3. Breeden, Joseph L. & Parker, Robert & Steinebach, Carsten, 2012. "A through-the-cycle model for retail lending economic capital," International Journal of Forecasting, Elsevier, vol. 28(1), pages 133-138.
    4. Yousra Tourki & Jeffrey Keisler & Igor Linkov, 2013. "Scenario analysis: a review of methods and applications for engineering and environmental systems," Environment Systems and Decisions, Springer, vol. 33(1), pages 3-20, March.

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